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Full Description
Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS.
FEATURES:
Provides the necessary background for newcomers to the field (both academics and interested practitioners)
Presents a timely snapshot of explainable and interpretable models in ITS applications
Discusses ethical, societal, and legal implications of adopting XAI in the context of ITS
Identifies future research directions and open problems
Contents
Section I Towards explainable ITS. 1. Explainable AI for Intelligent Transportation Systems: Are we there yet? Amina Adadi and Afaf Bouhoute. Section II Interpretable methods for ITS applications. 2. Towards Safe, Explainable, and Regulated Autonomous Driving Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, and Randy Goebel. 3. Explainable Machine Learning Method for Predicting Road Traffic Accident Injury Severity in Addis Ababa city based on a New Graph Feature Selection Technique Yassine Akhiat , Younes Bouchlaghem, Ahmed Zinedine, and Mohamed Chahhou. 4. COVID-19 pandemic effects on traffic crash patterns and in- juries in Barcelona, Spain: An interpretable approach Ahmad Aiash and Francesc Robuste. 5. Advances in Explainable Reinforcement Learning: an Intelligent Transportation Systems perspective Rudy Milani, Maximilian Moll and Stefan Pickl. 6. Road Traffic Data Collection: Handling Missing Data Abdelilah Mbarek, Mouna Jiber, Ali Yahyaouy, and Abdelouahed Sabri. 7. Explainability of surrogate models for traffic signal control Pawel Gora, Dominik Bogucki, and M. Latif Bolum. 8. Intelligent Techniques and Explainable Artificial Intelligence for Vessel Traffic Service: A Survey Meng Joo Er, Huibin Gong, Chuang Ma, Wenxiao Gao. 9. An Explainable Model for Detection and Recognition of Traffic Road Signs Anass Barodi, Abdelkarim Zemmouri, Abderrahim Bajit, Mohammed Benbrahim, and Ahmed Tamtaoui. 10. An Interpretable Detection of Transportation Mode Consider- ing GPS, Spatial, and Contextual Data based on Ensemble Machine Learning Sajjad Sowlati, Rahim Ali Abbaspour, and Chehreghan. 11. Blockchain and Explainable AI for Trustworthy Autonomous Vehicles Ouassima Markouh, Amina Adadi, Mohammed Berrada. Section III Ethical, social and legal implications of XAI in ITS. 12. Ethical Decision-Making Under Different Perspective-Taking Scenarios and Demographic Characteristics: The Case of Autonomous Vehicles Kareem Othman.
